{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:23:06Z","timestamp":1780323786721,"version":"3.54.1"},"reference-count":50,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T00:00:00Z","timestamp":1720915200000},"content-version":"vor","delay-in-days":195,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702249"],"award-info":[{"award-number":["61702249"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61741311"],"award-info":[{"award-number":["61741311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF134"],"award-info":[{"award-number":["ZR2020MF134"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Arrhythmia is a prevalent cardiovascular disease, which has garnered widespread attention due to its age\u2010related increases in mortality rates. In the analysis of arrhythmia, the electrocardiogram (ECG) plays an important role. Arrhythmia classification often suffers from a significant data imbalance issue due to the limited availability of data for certain arrhythmia categories. This imbalance problem significantly affects the classification performance of the model. To address this challenge, data augmentation emerges as a viable solution, aiming to neutralize the adverse effects of imbalanced datasets on the model. To this end, this paper proposes a novel Multimodality Data Augmentation Network (MM\u2010DANet) for arrhythmia classification. The MM\u2010DANet consists of two modules: the multimodality data matching\u2010based data augmentation module and the multimodality feature encoding module. In the multimodality data matching\u2010based data augmentation module, we expand the underrepresented arrhythmia categories to match the size of the largest category. Subsequently, the multimodality feature encoding module employs convolutional neural networks (CNN) to extract the modality\u2010specific features from both signals and images and concatenate them for efficient and accurate classification. The MM\u2010DANet was evaluated on the MIT\u2010BIH Arrhythmia Database and achieving an accuracy of 98.83%, along with an average specificity of 98.87%, average sensitivity of 92.92%, average precision of 91.05%, and average <jats:italic>F<\/jats:italic>1_score of 91.96%. Furthermore, its performance was also assessed on the St. Petersburg INCART arrhythmia database and the MIT\u2010BIH supraventricular arrhythmia database, yielding AUC values of 81.98% and 90.93%, respectively. These outstanding results not only underscore the effectiveness of MM\u2010DANet but also indicate its potential for facilitating reliable automated analysis of arrhythmias.<\/jats:p>","DOI":"10.1155\/2024\/9954821","type":"journal-article","created":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T16:18:40Z","timestamp":1720973920000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multimodality Data Augmentation Network for Arrhythmia Classification"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0879-2703","authenticated-orcid":false,"given":"Zhimin","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5868-6023","authenticated-orcid":false,"given":"Mujun","family":"Zang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shusen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chanjuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,7,14]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.101874"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.05.013"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40846-022-00687-7"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103726","article-title":"Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review","volume":"102","author":"Murat F.","year":"2020","journal-title":"Computers in Biology and Medicine"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.05.034"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbw068"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswax.2020.100033"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.23041"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"YanG. ShenL. ZhangY. andFanL. Fusing transformer model with temporal features for ECG heartbeat classification 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) November 2019 San Diego CA USA IEEE 898\u2013905.","DOI":"10.1109\/BIBM47256.2019.8983326"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2833841"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104206"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2021.04.002"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2930882"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18072090"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.11.010"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2974712"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1142\/s0219519419500040"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_2_8_19_2","first-page":"2980","article-title":"Focal loss for dense object detection","volume":"42","author":"Lin T.-Yi","year":"2017","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101675"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06487-5"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2931500"},{"key":"e_1_2_8_23_2","doi-asserted-by":"crossref","unstructured":"AdibE. AfghahF. andPrevostJ. J. Arrhythmia classification using CGAN-augmented ECG signals 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) December 2022 Las Vegas NV USA IEEE 1865\u20131872.","DOI":"10.1109\/BIBM55620.2022.9995088"},{"key":"e_1_2_8_24_2","first-page":"5767","article-title":"Improved training of wasserstein GANs","volume":"30","author":"Ishaan G.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_8_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02779-1"},{"key":"e_1_2_8_26_2","doi-asserted-by":"crossref","unstructured":"Bari\u0161i\u0107M.andJovi\u0107A. Cardiac arrhythmia classification from 12-lead electrocardiogram using a combination of deep learning approaches 2022 45th Jubilee International Convention on Information Communication and Electronic Technology (MIPRO) May 2022 Opatija Croatia IEEE 1489\u20131494.","DOI":"10.23919\/MIPRO55190.2022.9803539"},{"key":"e_1_2_8_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.08.022"},{"key":"e_1_2_8_28_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6320651"},{"key":"e_1_2_8_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105325"},{"key":"e_1_2_8_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103866"},{"key":"e_1_2_8_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106533"},{"key":"e_1_2_8_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.08.156"},{"key":"e_1_2_8_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.02.035"},{"key":"e_1_2_8_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"e_1_2_8_35_2","doi-asserted-by":"publisher","DOI":"10.1161\/01.cir.101.23.e215"},{"key":"e_1_2_8_36_2","volume-title":"R2008-testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms","author":"Ansi A. A. M. I.","year":"2008"},{"key":"e_1_2_8_37_2","article-title":"Adam: a method for stochastic optimization","author":"Kingma D. P.","year":"2015","journal-title":"3rd International Conference on Learning Representations (ICLR)"},{"key":"e_1_2_8_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.03.025"},{"key":"e_1_2_8_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.12.004"},{"key":"e_1_2_8_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2012.10.005"},{"key":"e_1_2_8_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106582"},{"key":"e_1_2_8_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102262"},{"key":"e_1_2_8_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102968"},{"key":"e_1_2_8_44_2","first-page":"1","article-title":"Deep multi-label multi-instance classification on 12-lead ecg","author":"Feng Y.","year":"2020","journal-title":"2020 Computing in Cardiology"},{"key":"e_1_2_8_45_2","doi-asserted-by":"crossref","unstructured":"LiX. ZhouY. WangJ. LinH. ZhaoJ. DingD. YuW. andChenY. Multi-modal multi-instance learning for retinal disease recognition Proceedings of the 29th ACM International Conference on Multimedia October 2021 Chengdu China 2474\u20132482.","DOI":"10.1145\/3474085.3475418"},{"key":"e_1_2_8_46_2","first-page":"2136","article-title":"Transmil: transformer based correlated multiple instance learning for whole slide image classification","volume":"34","author":"Shao Z.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_8_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110555"},{"key":"e_1_2_8_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/taslp.2020.3030497"},{"key":"e_1_2_8_49_2","first-page":"113","article-title":"Stochastic gradient descent","author":"Nikhil K.","year":"2017","journal-title":"Deep Learning with Python"},{"key":"e_1_2_8_50_2","first-page":"26","article-title":"Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude","volume":"4","author":"Tieleman T.","year":"2012","journal-title":"COURSERA: Neural networks for machine learning"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2024\/9954821","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T16:18:50Z","timestamp":1720973930000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2024\/9954821"}},"subtitle":[],"editor":[{"given":"Surya","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1155\/2024\/9954821"],"URL":"https:\/\/doi.org\/10.1155\/2024\/9954821","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2022-12-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9954821"}}